ผลต่างระหว่างรุ่นของ "Probstat/notes/regression"
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== Model == | == Model == | ||
+ | We consider two variables <math>X</math> and <math>Y</math> where <math>Y</math> is a function of <math>X</math>. We refer to <math>X</math> as independent or input variable, and <math>Y</math> as a dependent variable. We consider linear relationship between independent variable and dependent variable. We assume that there exist hidden variables <math>\alpha</math> and <math>\beta</math> such that | ||
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+ | <center> | ||
+ | <math>Y = \alpha + \beta\cdot X + e,</math> | ||
+ | </center> | ||
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+ | where <math>e</math> is a random error. We further assume that the error is unbiased, i.e., <math>E[e]=0</math> and is independent of <math>X</math>. | ||
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== The least squares estimators == | == The least squares estimators == | ||
== Distribution of regression parameters == | == Distribution of regression parameters == | ||
== Statistical tests on regression parameters == | == Statistical tests on regression parameters == |
รุ่นแก้ไขเมื่อ 09:03, 7 ธันวาคม 2557
- This is part of probstat.
In this section, we shall discuss linear regression. We shall focus on one-variable linear regression.
เนื้อหา
Model
We consider two variables and where is a function of . We refer to as independent or input variable, and as a dependent variable. We consider linear relationship between independent variable and dependent variable. We assume that there exist hidden variables and such that
where is a random error. We further assume that the error is unbiased, i.e., and is independent of .